Pub Date : 2024-11-22DOI: 10.1016/j.adapen.2024.100198
Sebastian Zwickl-Bernhard
This paper addresses the currently overlooked yet urgent topic of material recycling and remanufacturing in energy system optimization modeling, making three substantial contributions. First, it presents a comprehensive review of relevant studies on material demand, flows, and recycling from a techno-economic perspective and highlights the critical gap in existing energy system optimization models, in which material recycling and remanufacturing is not yet adequately integrated. Second, the paper introduces a general mathematical framework for incorporating material recycling and remanufacturing as a technology and investment option into typical energy system optimization models. Third, the paper demonstrates the practical application of this framework by examining the material recycling potential within the solar module expansion plan of the European Union. It explores the main drivers under which material recycling becomes economically competitive, considering various global and regional solar market conditions. Specifically, it investigates how different energy policies — such as incentivizing European Union manufacturing, limiting import shares, and implementing a circular economy constraint — affect the optimal remanufacturing capacities and achievable shares of recycling-based additions to meet the expansion targets until 2050.
{"title":"Integrating material recycling and remanufacturing in energy system optimization modeling: A review and showcase","authors":"Sebastian Zwickl-Bernhard","doi":"10.1016/j.adapen.2024.100198","DOIUrl":"10.1016/j.adapen.2024.100198","url":null,"abstract":"<div><div>This paper addresses the currently overlooked yet urgent topic of material recycling and remanufacturing in energy system optimization modeling, making three substantial contributions. First, it presents a comprehensive review of relevant studies on material demand, flows, and recycling from a techno-economic perspective and highlights the critical gap in existing energy system optimization models, in which material recycling and remanufacturing is not yet adequately integrated. Second, the paper introduces a general mathematical framework for incorporating material recycling and remanufacturing as a technology and investment option into typical energy system optimization models. Third, the paper demonstrates the practical application of this framework by examining the material recycling potential within the solar module expansion plan of the European Union. It explores the main drivers under which material recycling becomes economically competitive, considering various global and regional solar market conditions. Specifically, it investigates how different energy policies — such as incentivizing European Union manufacturing, limiting import shares, and implementing a circular economy constraint — affect the optimal remanufacturing capacities and achievable shares of recycling-based additions to meet the expansion targets until 2050.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100198"},"PeriodicalIF":13.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1016/j.adapen.2024.100199
Ken Chen , Kongfu Hu , Hu Li , Siyan Chan , Junjie Chen , Yu Pei , Bin Zhao , Gang Pei
Photovoltaic/thermal (PV/T) hybrid technology offers significant potential for carbon neutrality by simultaneously converting photons into electricity and heat simultaneously. However, the mismatch between PV/T output temperature and the temperature demand across a wide range of scenarios limits its practical uses. Traditional PV cells have high infrared emissivity, resulting in significant heat losses and seriously significantly hindering the development of PV/T systems. Spectrally selective solar cells characterized by high solar absorption, low thermal emission, and photoelectric conversion process, have yet to be realized thus far. In this study, we propose an integrated design and develop a scalable industrial approach for fabricating meter-scale spectrally selective solar cell with a high solar absorptivity of 92.3 % and a low infrared emissivity of 20.3 %, achieving the highest absorption-emission ratio of measured 4.6 experimentally. The primary novelty of the design lies in integrating the PV cell electrode atop and low-emissivity layer into one eliminating the need for rare metals and reducing complexity. Furthermore, we demonstrate that the spectrally selective PV/T significantly increases the overall solar efficiency from 13.7 % to 82.5 % and reduces the heat loss coefficient to 3.55 W/(m2.K). The validated model accurately captures the high photovoltaic thermal efficiency, enabling new technological advancements.
{"title":"Scalable spectrally selective solar cell for highly efficient photovoltaic thermal conversion","authors":"Ken Chen , Kongfu Hu , Hu Li , Siyan Chan , Junjie Chen , Yu Pei , Bin Zhao , Gang Pei","doi":"10.1016/j.adapen.2024.100199","DOIUrl":"10.1016/j.adapen.2024.100199","url":null,"abstract":"<div><div>Photovoltaic/thermal (PV/T) hybrid technology offers significant potential for carbon neutrality by simultaneously converting photons into electricity and heat simultaneously. However, the mismatch between PV/T output temperature and the temperature demand across a wide range of scenarios limits its practical uses. Traditional PV cells have high infrared emissivity, resulting in significant heat losses and seriously significantly hindering the development of PV/T systems. Spectrally selective solar cells characterized by high solar absorption, low thermal emission, and photoelectric conversion process, have yet to be realized thus far. In this study, we propose an integrated design and develop a scalable industrial approach for fabricating meter-scale spectrally selective solar cell with a high solar absorptivity of 92.3 % and a low infrared emissivity of 20.3 %, achieving the highest absorption-emission ratio of measured 4.6 experimentally. The primary novelty of the design lies in integrating the PV cell electrode atop and low-emissivity layer into one eliminating the need for rare metals and reducing complexity. Furthermore, we demonstrate that the spectrally selective PV/T significantly increases the overall solar efficiency from 13.7 % to 82.5 % and reduces the heat loss coefficient to 3.55 W/(m<sup>2.</sup>K). The validated model accurately captures the high photovoltaic thermal efficiency, enabling new technological advancements.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100199"},"PeriodicalIF":13.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-28DOI: 10.1016/j.adapen.2024.100196
B. Koirala , H. Cai , F. Khayatian , E. Munoz , J.G. An , R. Mutschler , M. Sulzer , C. De Wolf , K. Orehounig
Urban multi-energy systems (UMES) incorporating distributed energy resources are vital to future low-carbon energy systems. These systems demand complex solutions, including increased integration of renewables, improved efficiency through electrification, and exploitation of synergies via sector coupling across multiple sectors and infrastructures. Digitalization and the Internet of Things bring new opportunities for the design-build-operate workflow of the cyber-physical urban multi-energy systems. In this context, digital twins are expected to play a crucial role in managing the intricate integration of assets, systems, and actors within urban multi-energy systems. This review explores digital twin opportunities for urban multi-energy systems by first considering the challenges of urban multi energy systems. It then reviews recent advancements in digital twin architectures, energy system data categories, semantic ontologies, and data management solutions, addressing the growing data demands and modelling complexities. Digital twins provide an objective and comprehensive information base covering the entire design, operation, decommissioning, and reuse lifecycle phases, enhancing collaborative decision-making among stakeholders. This review also highlights that future research should focus on scaling digital twins to manage the complexities of urban environments. A key challenge remains in identifying standardized ontologies for seamless data exchange and interoperability between energy systems and sectors. As the technology matures, future research is required to explore the socio-economic and regulatory implications of digital twins, ensuring that the transition to smart energy systems is both technologically sound and socially equitable. The paper concludes by making a series of recommendations on how digital twins could be implemented for urban multi energy systems.
{"title":"Digitalization of urban multi-energy systems – Advances in digital twin applications across life-cycle phases","authors":"B. Koirala , H. Cai , F. Khayatian , E. Munoz , J.G. An , R. Mutschler , M. Sulzer , C. De Wolf , K. Orehounig","doi":"10.1016/j.adapen.2024.100196","DOIUrl":"10.1016/j.adapen.2024.100196","url":null,"abstract":"<div><div>Urban multi-energy systems (UMES) incorporating distributed energy resources are vital to future low-carbon energy systems. These systems demand complex solutions, including increased integration of renewables, improved efficiency through electrification, and exploitation of synergies via sector coupling across multiple sectors and infrastructures. Digitalization and the Internet of Things bring new opportunities for the design-build-operate workflow of the cyber-physical urban multi-energy systems. In this context, digital twins are expected to play a crucial role in managing the intricate integration of assets, systems, and actors within urban multi-energy systems. This review explores digital twin opportunities for urban multi-energy systems by first considering the challenges of urban multi energy systems. It then reviews recent advancements in digital twin architectures, energy system data categories, semantic ontologies, and data management solutions, addressing the growing data demands and modelling complexities. Digital twins provide an objective and comprehensive information base covering the entire design, operation, decommissioning, and reuse lifecycle phases, enhancing collaborative decision-making among stakeholders. This review also highlights that future research should focus on scaling digital twins to manage the complexities of urban environments. A key challenge remains in identifying standardized ontologies for seamless data exchange and interoperability between energy systems and sectors. As the technology matures, future research is required to explore the socio-economic and regulatory implications of digital twins, ensuring that the transition to smart energy systems is both technologically sound and socially equitable. The paper concludes by making a series of recommendations on how digital twins could be implemented for urban multi energy systems.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100196"},"PeriodicalIF":13.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-28DOI: 10.1016/j.adapen.2024.100197
Haizhi Luo , Yiwen Zhang , Xinyu Gao , Zhengguang Liu , Xiangzhao Meng , Xiaohu Yang
The prediction of electricity consumption plays a vital role in promoting sustainable development, ensuring energy security and resilience, facilitating regional planning, and integrating renewable energy sources. A novel electricity consumption characterization and prediction model based on land use was proposed. This model achieves land-use subdivision to provide highly correlated variables; exhibits strong interpretability, thereby revealing even marginal effects of land use on electricity consumption; and demonstrates high performance, thereby enabling large-scale simulations and predictions. Using 297 cities and 2,505 counties as case studies, the key findings are as follows: (1) The model demonstrates strong generalization ability (R2 = 0.91), high precision (Kappa = 0.77), and robustness, with an overall prediction accuracy exceeding 80 %; (2) The marginal impact of industrial land on electricity consumption is more complex, with more efficiency achieved by limiting its area to either 104.3 km2 or between 288.2 and 657.3 km2; (3) The marginal impact of commercial and residential land on electricity consumption exhibits a strong linear relationship (R2 > 0.80). Restricting the scale to 11.3 km2 could effectively mitigate this impact. Mixed commercial and residential land is advantageous for overall electricity consumption control, but after exceeding 43.5 km2, separate layout considerations for urban residential land are necessary; (4) In 2030, Shanghai's electricity consumption is projected to reach 155,143 million kW·h, making it the highest among the 297 cities. Meanwhile, Suzhou Industrial Park leads among the 2,505 districts with a consumption of 30,996 million kW·h; (5) Identify future electricity consumption hotspots and clustering characteristics, evaluate the renewable energy potential in these hotspot areas, and propose targeted strategies accordingly.
{"title":"Multi-scale electricity consumption prediction model based on land use and interpretable machine learning: A case study of China","authors":"Haizhi Luo , Yiwen Zhang , Xinyu Gao , Zhengguang Liu , Xiangzhao Meng , Xiaohu Yang","doi":"10.1016/j.adapen.2024.100197","DOIUrl":"10.1016/j.adapen.2024.100197","url":null,"abstract":"<div><div>The prediction of electricity consumption plays a vital role in promoting sustainable development, ensuring energy security and resilience, facilitating regional planning, and integrating renewable energy sources. A novel electricity consumption characterization and prediction model based on land use was proposed. This model achieves land-use subdivision to provide highly correlated variables; exhibits strong interpretability, thereby revealing even marginal effects of land use on electricity consumption; and demonstrates high performance, thereby enabling large-scale simulations and predictions. Using 297 cities and 2,505 counties as case studies, the key findings are as follows: (1) The model demonstrates strong generalization ability (R<sup>2</sup> = 0.91), high precision (Kappa = 0.77), and robustness, with an overall prediction accuracy exceeding 80 %; (2) The marginal impact of industrial land on electricity consumption is more complex, with more efficiency achieved by limiting its area to either 104.3 km<sup>2</sup> or between 288.2 and 657.3 km<sup>2</sup>; (3) The marginal impact of commercial and residential land on electricity consumption exhibits a strong linear relationship (R<sup>2</sup> > 0.80). Restricting the scale to 11.3 km<sup>2</sup> could effectively mitigate this impact. Mixed commercial and residential land is advantageous for overall electricity consumption control, but after exceeding 43.5 km<sup>2</sup>, separate layout considerations for urban residential land are necessary; (4) In 2030, Shanghai's electricity consumption is projected to reach 155,143 million kW·h, making it the highest among the 297 cities. Meanwhile, Suzhou Industrial Park leads among the 2,505 districts with a consumption of 30,996 million kW·h; (5) Identify future electricity consumption hotspots and clustering characteristics, evaluate the renewable energy potential in these hotspot areas, and propose targeted strategies accordingly.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100197"},"PeriodicalIF":13.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-24DOI: 10.1016/j.adapen.2024.100194
Elke Schropp, Gabriel Naumann, Matthias Gaderer
Hydrogen is considered an essential component in mitigating climate change. Water electrolysis technologies present the potential for generating environmentally friendly hydrogen. The solid oxide water electrolysis attracts attention due to its high-temperature operation, leading to an unsurpassed efficiency. Nevertheless, high-temperature operation requires special materials, raising material criticality concerns. This study aims to determine the optimum current density for future solid oxide water electrolysis operation. To this end, the energetic performance of solid oxide electrolysis is assessed under different current densities with a numerical simulation. Consequently, prospective life cycle assessments and product-level material criticality assessments are performed. These dimensions are combined in a multi-criteria optimization. The environmental impacts strongly depend on electricity and heat generation, whereas manufacturing and the feed water supply play a minor role. Heat integration, a unique feature of solid oxide water electrolysis, is beneficial if heat carries less environmental impact than electricity. Then, the solid oxide electrolysis should be operated at relatively low current densities. In contrast, the material criticality decreases with increasing current densities. The multi-criteria optimization reveals that if minimizing environmental impacts and material criticality is equally vital, solid oxide water electrolysis should be operated at 0.955 A/cm2, whereas a focus on environmental impacts leads to lower current densities. In conclusion, the energy supply situation affects the operational current density from an environmental perspective. In contrast, the material criticality favors high current densities for solid oxide water electrolysis. When combining both, medium current densities lead to minimum environmental and material criticality issues.
{"title":"Hydrogen production via solid oxide electrolysis: Balancing environmental issues and material criticality","authors":"Elke Schropp, Gabriel Naumann, Matthias Gaderer","doi":"10.1016/j.adapen.2024.100194","DOIUrl":"10.1016/j.adapen.2024.100194","url":null,"abstract":"<div><div>Hydrogen is considered an essential component in mitigating climate change. Water electrolysis technologies present the potential for generating environmentally friendly hydrogen. The solid oxide water electrolysis attracts attention due to its high-temperature operation, leading to an unsurpassed efficiency. Nevertheless, high-temperature operation requires special materials, raising material criticality concerns. This study aims to determine the optimum current density for future solid oxide water electrolysis operation. To this end, the energetic performance of solid oxide electrolysis is assessed under different current densities with a numerical simulation. Consequently, prospective life cycle assessments and product-level material criticality assessments are performed. These dimensions are combined in a multi-criteria optimization. The environmental impacts strongly depend on electricity and heat generation, whereas manufacturing and the feed water supply play a minor role. Heat integration, a unique feature of solid oxide water electrolysis, is beneficial if heat carries less environmental impact than electricity. Then, the solid oxide electrolysis should be operated at relatively low current densities. In contrast, the material criticality decreases with increasing current densities. The multi-criteria optimization reveals that if minimizing environmental impacts and material criticality is equally vital, solid oxide water electrolysis should be operated at 0.955 A/cm<sup>2</sup>, whereas a focus on environmental impacts leads to lower current densities. In conclusion, the energy supply situation affects the operational current density from an environmental perspective. In contrast, the material criticality favors high current densities for solid oxide water electrolysis. When combining both, medium current densities lead to minimum environmental and material criticality issues.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100194"},"PeriodicalIF":13.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142653887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bidirectional charging, such as Vehicle-to-Grid, is increasingly seen as a way to integrate the growing number of battery electric vehicles into the energy system. The electrical storage capacity in the system can be enhanced by using electric vehicles as flexible storage units. However, large-scale applications of Vehicle-to-Grid may require significant expansion of distribution grids. Previous studies lack a comprehensive environmental assessment of related impacts. Contributing to this research gap, this article combines techno-economic grid simulations with scenario-based Life Cycle Assessments. The case study focuses on rural distribution grids in Southern Germany, projecting the repercussions of different charging scenarios by 2040. Besides a Vehicle-to-Grid scenario, a mixed scenario of Vehicle-to-Home, Vehicle-to-Grid, and direct charging is investigated. Results indicate that Vehicle-to-Grid charging increases grid impacts due to higher charging simultaneities and power losses, especially when following spot market prices. Despite these challenges, the secondary use of battery electric vehicles as storage units can offset adverse environmental effects. Bidirectional charging allows for higher use of volatile renewable energies and can accelerate their integration into the power system. When considering these diverse environmental effects, bidirectional charging scenarios show overall lower impacts on climate change per battery electric vehicle compared to direct charging. The insights provided are valuable for researchers, industry, utilities, and policymakers to understand the potential positive and negative impacts of large-scale battery electric vehicle integration. The article highlights the most influential parameters that should be considered before large-scale penetration.
{"title":"Green light for bidirectional charging? Unveiling grid repercussions and life cycle impacts","authors":"Daniela Wohlschlager , Janis Reinhard , Iris Stierlen , Anika Neitz-Regett , Magnus Fröhling","doi":"10.1016/j.adapen.2024.100195","DOIUrl":"10.1016/j.adapen.2024.100195","url":null,"abstract":"<div><div>Bidirectional charging, such as Vehicle-to-Grid, is increasingly seen as a way to integrate the growing number of battery electric vehicles into the energy system. The electrical storage capacity in the system can be enhanced by using electric vehicles as flexible storage units. However, large-scale applications of Vehicle-to-Grid may require significant expansion of distribution grids. Previous studies lack a comprehensive environmental assessment of related impacts. Contributing to this research gap, this article combines techno-economic grid simulations with scenario-based Life Cycle Assessments. The case study focuses on rural distribution grids in Southern Germany, projecting the repercussions of different charging scenarios by 2040. Besides a Vehicle-to-Grid scenario, a mixed scenario of Vehicle-to-Home, Vehicle-to-Grid, and direct charging is investigated. Results indicate that Vehicle-to-Grid charging increases grid impacts due to higher charging simultaneities and power losses, especially when following spot market prices. Despite these challenges, the secondary use of battery electric vehicles as storage units can offset adverse environmental effects. Bidirectional charging allows for higher use of volatile renewable energies and can accelerate their integration into the power system. When considering these diverse environmental effects, bidirectional charging scenarios show overall lower impacts on climate change per battery electric vehicle compared to direct charging. The insights provided are valuable for researchers, industry, utilities, and policymakers to understand the potential positive and negative impacts of large-scale battery electric vehicle integration. The article highlights the most influential parameters that should be considered before large-scale penetration.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100195"},"PeriodicalIF":13.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The growing electrification of heating and mobility has increased the interdependence of these two sectors and introduced a new coupling with the electricity sector. However, existing studies on local energy planning often focus solely on solutions to meet buildings’ energy demands, neglecting or highly simplifying new mobility demands. Here, we address this gap by introducing MANGOever (Multi-stAge eNerGy Optimization for electric vehicles and energy retrofits), a comprehensive optimization framework for long-term co-planning of building energy systems and electric vehicle (EV) charging infrastructure. The framework optimizes multi-stage investments and operational strategies to minimize system costs and CO emissions over a multi-year horizon, considering the stochastic nature of EV charging based on observed driver habits and travel patterns. Applying the model to a case study of a multi-family home in Switzerland reveals significant synergies between EV charging and the management of solar photovoltaic generation. The results underscore the importance of considering habit-based EV charging behavior in the model and demonstrate how diverse EV plug-in behaviors can be leveraged to maximize the use of midday solar production and reduce emissions. These findings emphasize the need for integrated planning of these sectors to achieve a cost-effective, low-carbon energy transition.
供暖和交通日益电气化，增加了这两个部门的相互依存性，并与电力部门产生了新的联系。然而，现有的地方能源规划研究往往只关注满足建筑物能源需求的解决方案，忽视或高度简化了新的交通需求。在此，我们引入了 MANGOever（电动汽车和能源改造的多阶段 eNerGy 优化）来弥补这一不足，它是一个综合优化框架，用于建筑能源系统和电动汽车（EV）充电基础设施的长期共同规划。该框架根据观察到的驾驶员习惯和出行模式，考虑到电动汽车充电的随机性，对多阶段投资和运营策略进行优化，以在多年期限内最大限度地降低系统成本和二氧化碳排放量。将该模型应用于瑞士的一个多户住宅案例研究，发现电动汽车充电与太阳能光伏发电管理之间存在显著的协同效应。研究结果强调了在模型中考虑基于习惯的电动汽车充电行为的重要性，并展示了如何利用不同的电动汽车插电行为最大限度地利用中午的太阳能发电并减少排放。这些发现强调了对这些部门进行综合规划的必要性，以实现具有成本效益的低碳能源转型。
{"title":"MANGOever: An optimization framework for the long-term planning and operations of integrated electric vehicle and building energy systems","authors":"Alicia Lerbinger , Siobhan Powell , Georgios Mavromatidis","doi":"10.1016/j.adapen.2024.100193","DOIUrl":"10.1016/j.adapen.2024.100193","url":null,"abstract":"<div><div>The growing electrification of heating and mobility has increased the interdependence of these two sectors and introduced a new coupling with the electricity sector. However, existing studies on local energy planning often focus solely on solutions to meet buildings’ energy demands, neglecting or highly simplifying new mobility demands. Here, we address this gap by introducing MANGOever (Multi-stAge eNerGy Optimization for <strong>e</strong>lectric <strong>v</strong>ehicles and <strong>e</strong>nergy <strong>r</strong>etrofits), a comprehensive optimization framework for long-term co-planning of building energy systems and electric vehicle (EV) charging infrastructure. The framework optimizes multi-stage investments and operational strategies to minimize system costs and CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions over a multi-year horizon, considering the stochastic nature of EV charging based on observed driver habits and travel patterns. Applying the model to a case study of a multi-family home in Switzerland reveals significant synergies between EV charging and the management of solar photovoltaic generation. The results underscore the importance of considering habit-based EV charging behavior in the model and demonstrate how diverse EV plug-in behaviors can be leveraged to maximize the use of midday solar production and reduce emissions. These findings emphasize the need for integrated planning of these sectors to achieve a cost-effective, low-carbon energy transition.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100193"},"PeriodicalIF":13.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-19DOI: 10.1016/j.adapen.2024.100192
Johannes Behrens , Elisabeth Zeyen , Maximilian Hoffmann , Detlef Stolten , Jann M. Weinand
Energy system components like renewable energy technologies or electrolyzers are subject to decreasing investment costs driven by technological progress. Various methods have been developed in the literature to capture model-endogenous technological learning. This review demonstrates the non-linear relationship between investment costs and production volume, resulting in non-convex optimization problems and discuss concepts to account for technological progress. While iterative solution methods tend to find future energy system designs that rely on suboptimal technology mixes, exact solutions leading to global optimality are computationally demanding. Most studies omit important system aspects such as sector integration, or a detailed spatial, temporal, and technological resolution to maintain model solvability, which likewise distorts the impact of technological learning. This can be improved by the application of methods such as temporal or spatial aggregation, decomposition methods, or the clustering of technologies. This review reveals the potential of those methods and points out important considerations for integrating endogenous technological learning. We propose a more integrated approach to handle computational complexity when integrating technological learning, that aims to preserve the model's feasibility. Furthermore, we identify significant gaps in current modeling practices and suggest future research directions to enhance the accuracy and utility of energy system models.
{"title":"Reviewing the complexity of endogenous technological learning for energy system modeling","authors":"Johannes Behrens , Elisabeth Zeyen , Maximilian Hoffmann , Detlef Stolten , Jann M. Weinand","doi":"10.1016/j.adapen.2024.100192","DOIUrl":"10.1016/j.adapen.2024.100192","url":null,"abstract":"<div><div>Energy system components like renewable energy technologies or electrolyzers are subject to decreasing investment costs driven by technological progress. Various methods have been developed in the literature to capture model-endogenous technological learning. This review demonstrates the non-linear relationship between investment costs and production volume, resulting in non-convex optimization problems and discuss concepts to account for technological progress. While iterative solution methods tend to find future energy system designs that rely on suboptimal technology mixes, exact solutions leading to global optimality are computationally demanding. Most studies omit important system aspects such as sector integration, or a detailed spatial, temporal, and technological resolution to maintain model solvability, which likewise distorts the impact of technological learning. This can be improved by the application of methods such as temporal or spatial aggregation, decomposition methods, or the clustering of technologies. This review reveals the potential of those methods and points out important considerations for integrating endogenous technological learning. We propose a more integrated approach to handle computational complexity when integrating technological learning, that aims to preserve the model's feasibility. Furthermore, we identify significant gaps in current modeling practices and suggest future research directions to enhance the accuracy and utility of energy system models.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100192"},"PeriodicalIF":13.0,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-30DOI: 10.1016/j.adapen.2024.100191
Matthias Zech , Hendrik-Pieter Tetens , Joseph Ranalli
It is crucial to know the location of rooftop PV systems to monitor the regional progress toward sustainable societies and to ensure the integration of decentralized energy resources into the electricity grid. However, locations of PV are often unknown, which is why a large number of studies have proposed variants of Deep Learning to detect PV panels in remote sensing data using supervised Deep Learning. However, these methods are based on annotating datasets and therefore often require relabeling when fine-tuned or extended to a different region. Recent advances in Deep Active Learning offer the opportunity to significantly reduce the number of required annotated images by intelligently selecting the images to label next based on their informative value for the model. In this study, we compare different Deep Active Learning algorithms using a variety of datasets from different regions and compare different model training variants. In the simulations, the entropy-based acquisition function shows the highest performance with only 3% of the data needed in case-imbalanced data, while remaining simple to implement. We believe that Deep Active Learning provides an elegant solution to maintain high model accuracy while reducing annotation effort substantially. This facilitates the development of generalizable models for worldwide rooftop PV detection.
{"title":"Toward global rooftop PV detection with Deep Active Learning","authors":"Matthias Zech , Hendrik-Pieter Tetens , Joseph Ranalli","doi":"10.1016/j.adapen.2024.100191","DOIUrl":"10.1016/j.adapen.2024.100191","url":null,"abstract":"<div><div>It is crucial to know the location of rooftop PV systems to monitor the regional progress toward sustainable societies and to ensure the integration of decentralized energy resources into the electricity grid. However, locations of PV are often unknown, which is why a large number of studies have proposed variants of Deep Learning to detect PV panels in remote sensing data using supervised Deep Learning. However, these methods are based on annotating datasets and therefore often require relabeling when fine-tuned or extended to a different region. Recent advances in Deep Active Learning offer the opportunity to significantly reduce the number of required annotated images by intelligently selecting the images to label next based on their informative value for the model. In this study, we compare different Deep Active Learning algorithms using a variety of datasets from different regions and compare different model training variants. In the simulations, the entropy-based acquisition function shows the highest performance with only 3% of the data needed in case-imbalanced data, while remaining simple to implement. We believe that Deep Active Learning provides an elegant solution to maintain high model accuracy while reducing annotation effort substantially. This facilitates the development of generalizable models for worldwide rooftop PV detection.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100191"},"PeriodicalIF":13.0,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-20DOI: 10.1016/j.adapen.2024.100190
Maximilian Hoffmann , Bruno U. Schyska , Julian Bartels , Tristan Pelser , Johannes Behrens , Manuel Wetzel , Hans Christian Gils , Chuen-Fung Tang , Marius Tillmanns , Jan Stock , André Xhonneux , Leander Kotzur , Aaron Praktiknjo , Thomas Vogt , Patrick Jochem , Jochen Linßen , Jann M. Weinand , Detlef Stolten
Optimization-based frameworks for energy system modeling such as TIMES, ETHOS.FINE, or PyPSA have emerged as important tools to outline a cost-efficient energy transition. Consequently, numerous reviews have compared the capabilities and application cases of established energy system optimization frameworks with respect to their model features or adaptability but widely neglect the frameworks’ underlying mathematical structure. This limits their added value for users who not only want to use models but also program them themselves.
To address this issue, we follow a hybrid approach by not only reviewing 63 optimization-based frameworks for energy system modeling with a focus on their mathematical implementation but also conducting a meta-review of 68 existing literature reviews.
Our work reveals that the basic concept of network-based energy flow optimization has remained the same since the earliest publications in the 1970s. Thereby, the number of open-source available optimization frameworks for energy system modeling has more than doubled in the last ten years, mainly driven by the uptake of energy transition and progress in computer-aided optimization.
To go beyond a qualitative discussion, we also define the mathematical formulation for a mixed-integer optimization model comprising all the model features discussed in this work. We thereby aim to facilitate the implementation of future object-oriented frameworks and to increase the comprehensibility of existing ones for energy system modelers.
{"title":"A review of mixed-integer linear formulations for framework-based energy system models","authors":"Maximilian Hoffmann , Bruno U. Schyska , Julian Bartels , Tristan Pelser , Johannes Behrens , Manuel Wetzel , Hans Christian Gils , Chuen-Fung Tang , Marius Tillmanns , Jan Stock , André Xhonneux , Leander Kotzur , Aaron Praktiknjo , Thomas Vogt , Patrick Jochem , Jochen Linßen , Jann M. Weinand , Detlef Stolten","doi":"10.1016/j.adapen.2024.100190","DOIUrl":"10.1016/j.adapen.2024.100190","url":null,"abstract":"<div><div>Optimization-based frameworks for energy system modeling such as TIMES, ETHOS.FINE, or PyPSA have emerged as important tools to outline a cost-efficient energy transition. Consequently, numerous reviews have compared the capabilities and application cases of established energy system optimization frameworks with respect to their model features or adaptability but widely neglect the frameworks’ underlying mathematical structure. This limits their added value for users who not only want to use models but also program them themselves.</div><div>To address this issue, we follow a hybrid approach by not only reviewing 63 optimization-based frameworks for energy system modeling with a focus on their mathematical implementation but also conducting a meta-review of 68 existing literature reviews.</div><div>Our work reveals that the basic concept of network-based energy flow optimization has remained the same since the earliest publications in the 1970s. Thereby, the number of open-source available optimization frameworks for energy system modeling has more than doubled in the last ten years, mainly driven by the uptake of energy transition and progress in computer-aided optimization.</div><div>To go beyond a qualitative discussion, we also define the mathematical formulation for a mixed-integer optimization model comprising all the model features discussed in this work. We thereby aim to facilitate the implementation of future object-oriented frameworks and to increase the comprehensibility of existing ones for energy system modelers.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100190"},"PeriodicalIF":13.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号